Quadruped-Sim

This repository contains an environment for simulating a quadruped robot.

Installation

Recommend using a virtualenv (or conda) with python3.6 or higher. After installing virtualenv with pip, this can be done as follows:

virtualenv {quad_env, or choose another name venv_name} --python=python3

To activate the virtualenv:

source {PATH_TO_VENV}/bin/activate

Your command prompt should now look like:

(venv_name) user@pc:path$

The repository depends on recent versions of pybullet, numpy, etc., which you can install inside your virtual environment with:

pip install -r requirements.txt

Code structure

• env for the quadruped environment files, please see the gym simulation environment quadruped_gym_env.py, the robot specific functionalities in quadruped.py, and config variables in configs_a1.py. You will need to make edits in quadruped_gym_env.py, and review quadruped.py carefully for accessing robot states and calling functions to solve inverse kinematics, return the leg Jacobian, etc.
• a1_description contains the robot mesh files and urdf.
• utils for some file i/o and plotting helpers.
• hopf_network.py provides a CPG class skeleton for various gaits, and maps these to be executed on an instance of the quadruped_gym_env class. Please fill in this file carefully.
• run_sb3.py and load_sb3.py provide an interface to training RL algorithms based on stable-baselines3. You should review the documentation carefully for information on the different algorithms and training hyperparameters.

Code resources

• The PyBullet Quickstart Guide is the current up-to-date documentation for interfacing with the simulation.
• The quadruped environment took inspiration from Google's motion-imitation repository based on this paper.
• Reinforcement learning algorithms from stable-baselines3. Also see for example ray[rllib] and spinningup.

Conceptual resources

The CPGs are based on the following papers:

• L. Righetti and A. J. Ijspeert, "Pattern generators with sensory feedback for the control of quadruped locomotion," 2008 IEEE International Conference on Robotics and Automation, 2008, pp. 819-824, doi: 10.1109/ROBOT.2008.4543306. link
• M. Ajallooeian, S. Pouya, A. Sproewitz and A. J. Ijspeert, "Central Pattern Generators augmented with virtual model control for quadruped rough terrain locomotion," 2013 IEEE International Conference on Robotics and Automation, 2013, pp. 3321-3328, doi: 10.1109/ICRA.2013.6631040. link
• M. Ajallooeian, S. Gay, A. Tuleu, A. Spröwitz and A. J. Ijspeert, "Modular control of limit cycle locomotion over unperceived rough terrain," 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 3390-3397, doi: 10.1109/IROS.2013.6696839. link